CODEWITHSUNDEEP
pythonpython-3.xkerasdeep-learning
BigD
BigD

Reputation: 581

Keras: ValueError: Error when checking target: expected dense_2 to have shape (10,) but got array with shape (1,)

I'm new to deep learning and Keras. When I used Keras to fit a LSTM model, I got the following error message: ValueError: Error when checking target: expected dense_2 to have shape (10,) but got array with shape (1,)

Here are my code for building LSTM:

def build(self, embedding_matrix, dim, num_class, vocab_size, maxlen):
    model = Sequential()
    model.add(Embedding(vocab_size, dim, weights = [embedding_matrix],
                        input_length = maxlen, trainable = False)) ## pre-trained model
    model.add(LSTM(dim))
    model.add(Dense(dim, activation = "relu"))
    model.add(Dense(num_class, activation = "softmax"))
    self.model = model

Before this post, I tried several solutions metioned in other SO posts. For example, use to_categorical to transform labels, use Flatten before the final layer. Sadly none of them worked.

Here are my log file for running the script:

Start to fit GLOVE with reported data
Applying GLOVE pre-trained model
Number of unique tokens: 308758
Pre-trained model finished

Applying keras text pre-processing
Finish to apply keras text pre-processing

Start to fit the model...

Build LSTM model...
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
embedding_1 (Embedding)      (None, 36303, 300)        92627400
_________________________________________________________________
lstm_1 (LSTM)                (None, 300)               721200
_________________________________________________________________
dense_1 (Dense)              (None, 300)               90300
_________________________________________________________________
dense_2 (Dense)              (None, 10)                3010
=================================================================
Total params: 93,441,910
Trainable params: 814,510
Non-trainable params: 92,627,400
_________________________________________________________________
None
Finish model building

It went well untill history = self.model.fit(train_padded, y_train, epochs = 10, batch_size = 128, validation_split = 0.2), then I got the above error.

I run out of solutions. Any help will be appriciate!

Edit:

About the y_train, here is the code I use for building y_train:

labels = dt["category"].values
num_class = len(np.unique(labels))
classes = np.unique(labels)
le = LabelEncoder()
y = le.fit_transform(labels)
y = to_categorical(y, num_class)
## split to training and test set
x_train, y_train, x_test, y_test = train_test_split(text, y, test_size = 0.33, 
                     random_state = 42, 
                     stratify = dt["category"].astype("str"))

Another update: here are the shapes.

The shape of y_train:  (48334,)
The shape of x_train:  (98132,)
The shape of y_test:  (48334, 10)
The shape of x_test:  (98132, 10)

Upvotes: 3

Views: 2058

Answers (1)

Athena
Athena

Reputation: 3228

The problem is that you are getting your x_train, y_train, x_test, y_test in the wrong order, so it is assigning things incorrectly. train_test_split returns the following:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,random_state=42)

whereas you have x_train, y_train, x_test, y_test

train_test_split docs

Upvotes: 1

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